Research on News Text Classification Based on Deep Learning Convolutional Neural Network

Yunlong Zhu,Yinghui Ye

doi:10.1155/2021/1508150

Abstract

Aiming at the problems of low classification accuracy and low efficiency of existing news text classification methods, a new method of news text classification based on deep learning convolutional neural network is proposed. Determine the weight of the news text data through the VSM (Viable System Model) vector space model, calculate the information gain of mutual information, and determine the characteristics of the news text data; on this basis, use the hash algorithm to encode the news text data to calculate any news. The spacing between the text data realizes the feature preprocessing of the news text data; this article analyzes the basic structure of the deep learning convolutional neural network, uses the convolutional layer in the convolutional neural network to determine the change value of the convolution kernel, trains the news text data, builds a news text classifier of deep learning convolutional neural network, and completes news text classification. The experimental results show that the deep learning convolutional neural network can improve the accuracy and speed of news text classification, which is feasible.

Highlights

With the rapid development of Internet technology, we are in the era of information explosion
In view of the shortcomings of the above methods, this paper proposes a research on news text classification based on deep learning convolutional neural network
The convolutional neural network structure used in this experiment mainly consists of 1 word embedding layer, 1 convolutional layer, and 1 pooling layer, the dimension of the word vector is set to 128, the size of the convolutional kernel window is set to 3 × 128,4 × 128,5 × 128, etc., and the number of convolutional nuclei is 128

Summary

Introduction

With the rapid development of Internet technology, we are in the era of information explosion. In view of the shortcomings of the above methods, this paper proposes a research on news text classification based on deep learning convolutional neural network. The technical route of this paper is as follows: Step 1: determine the weight of news text data through VSM vector space model, calculate the information gain of mutual information, and determine the characteristics of news text data. The preprocessing of the news text feature data based on the deep hashing algorithm is implemented, i.e., VI. In formula (12), the μik represents the weight value of the convolutional kernel In this network, because it is prone to certain errors in the forward propagation, the forward propagation in the neural network is corrected to achieve a more accurate classification of news text.

Experimental Analysis

Methods of this paper

Findings

Conclusion